The present invention relates to computer-based systems and methods of determining product parameters, performing process control, and/or classifying products based on analysis and prediction of responses to different tests, such as powdering tests on a coated substrate.
Presently, there is a substantial demand for coated products. One example of such coated products is galvanneal-coated steel. The demand for galvanneal coated steel spans various industries, largely the automotive industry. Within each industry, there are various consumers who require tests of different characteristics of the galvanneal-coated steel. There are significant differences among the various customer tests which determine whether a batch of galvanneal-coated steel meets a particular consumer's requirements.
In general, excessive powdering of the galvanneal coating is undesirable. Different consumers, however, require a resistance to powdering under different circumstances and therefore in response to different testing conditions.
In the automotive industry, for example, some of the major automotive manufacturers determine whether a shipment of galvanneal-coated steel satisfies their requirements by subjecting the galvanneal-coated steel to a V-bend test and determining whether the amount of powdering, if any, as a result of the V-bend test remains below a predetermined value. Some make this determination based on the results of a 90-degree-bend test. Still others apply a reverse Olsen test to the galvanneal-coated steel, and then determine whether the amount of powdering remains below the same or a different predetermined value.
In conducting a V-bend test, the sheet of galvanneal-coated steel is subjected to a free bend over a punch with a radius of 0.04 inch. The punch contacts the sheet only at certain locations while the rest of the sheet deforms with no tool contact. A visual rating then is performed on the compression side of the bend after unbending, or, alternatively, the samples are cleaned and weighed and a mass loss due to powdering is recorded. The standard 90-degree bend test is similar to the V-bend test, except that the punch radius is 0.12 inch and that the punch contacts the galvanneal-coated steel at all locations.
According to the reverse Olsen test, by contrast, the sheet of steel is dimpled first on one side using a 0.87 inch diameter ball to a height of 0.35 inch, and then the sheet is turned over and reverse dimpled to a height of 0.25 inch. Powdering is evaluated based on a visual rating on the outside of the dome after reverse dimpling, or alternatively the entire sample may be cleaned and measured for powdering mass loss.
The same specimen of galvanneal-coated steel may have different powdering tendencies in each of the different tests. Typically, the V-bend and reverse Olsen tests are used for testing light gage automotive products (usually 0.04 inch and less), while the 90 degree bend test is used to test both light and heavy gage galvanneal-coated products. Despite the typical uses for each test, each consumer chooses a particular one or combination of the tests. Not every consumer's choice, however, coincides with what is typical. When ordering a galvanneal-coated product, the consumer also might specify certain characteristics in the galvanneal-coated steel. Examples of such characteristics are the coating weight, iron content, gage, grade and/or coating phase composition.
It is then left to the manufacturer to produce a galvanneal-coated product which, in addition to having the specified characteristics, satisfies the particular powdering test(s) used by the consumer.
Heretofore, manufacturing and product parameters have been determined on a trial-and-error basis. Process control and classification of the resulting products also have been performed on a similar basis. The typical trial-and-error methodology involves setting certain manufacturing and product parameters, producing galvanneal-coated steel using such parameters, and testing the resulting galvanneal-coated steel to determine whether it satisfies the requirements of the particular consumer. This process typically requires several iterations before a satisfactory result is achieved. A considerable amount of time and resources therefore are expended before manufacturing of the actual product can even begin.
While some experienced manufacturers may have hunches about how to set the manufacturing and product parameters to generate a product containing the desired characteristics and resistance to powdering, such hunches often prove to be unreliable. The "hunch" method also is impractical because, even if it were reliable in some instances, it is limited to experienced manufacturers. Manufacturers who lack experience are far less likely to achieve favorable results using the "hunch" method.
Despite the disadvantages of the trial-and-error and hunch methods, the absence of any reliable alternative techniques for determining which manufacturing and product parameters will satisfy certain tests, has made the trial-and-error and hunch methods the primary source of manufacturing and product parameters. Considerable resources and time therefore are being wasted before the manufacturing process can even begin.
Once manufacturing begins, there is little guidance on how often a product should be tested to determine whether it continues to satisfy the particular consumer's set of requirements. Testing therefore might be performed at an insufficient frequency to detect flaws, or, alternatively, if the manufacturer is cautious, the testing might be performed too frequently. Since testing can be an expensive and time consuming process, testing at a frequency which is higher than necessary is wasteful. There is consequently a need for a method of determining how often to test a product based on a prediction of whether a particular product will provide poor, marginal, or good powdering resistance, and what the probability is that the particular product will fall within each category of poor, marginal, or good.
If a product fails to satisfy a particular consumer's requirements, there is little guidance on which parameters to modify and how to modify those parameters in order to maximize the likelihood that the resulting product will satisfy the particular consumer's requirements. The existing trial-and-error and hunch-based techniques are wasteful, as indicated above. A need therefore exists in the art for a method of performing process control based on an analysis and prediction of galvanneal powdering in different powdering tests.
There is also a need for a method of classifying products based on an analysis and prediction of galvanneal powdering in different powdering tests. Such classifications could be used to determine which powdering tests a particular product has a higher probability of satisfying. This information, in turn, could be used to determine which tests ultimately are performed on the product and to which consumer(s) such products might be acceptable. In this regard, the classifications could be used to determine distribution channels of the product. Since the present methods involve trial-and-error or hunch-based techniques, the existing methods of determining which tests are performed may result in an excessive number of tests. Time and resources therefore tend to be wasted under the present manufacturing schemes.
In order to avoid such waste, Applicants have attempted to predict, based on desired product characteristics and the particular powdering tests which are to be conducted on the product, which combinations of manufacturing and product parameters will prove to be successful under each of the different tests. Initially, data from over 1000 powdering test runs was compiled into a database, along with data on the coating and steel properties of the tested product. The data was obtained from research lab notebooks which, in turn, were used over a multiple year period to document powdering tests on a plurality of different samples.
Attempts were made to identify trends in these different tests based on large sample populations. Traditional regression analyses of the data, however, proved unsuccessful because of significant scatter which was observed in most of the powdering data. There is little, if any, benefit to being able to predict a value for powdering mass loss in a test, if a large prediction error band accompanies it. The same is true for other databases where only a few of the factors contributing to the measured response are known and are under control.
Another problem with using traditional regression analysis on the data arises because of differences in how the powdering was quantified in each of the various powdering tests. A visual ranking based on standard charts, for example, is a fast and simple method of quantifying powdering loss. This approach, however, is flawed to some extent in that the tape which is used in the ranking process only picks up the powder on the surface. The severity of the powdering therefore can be underestimated. In addition, a considerable overlap in ratings is observed when plotted against critical galvanneal coating variables, making it difficult to predict a future response.
When the severity of powdering is quantified using a mass loss quantification technique, instead of the visual rating technique, the results are far more accurate. The improvement in accuracy stems from the fact that the actual mass lost because of powdering is what gets measured. This technique, however, is time consuming and there is considerable scatter in the measured mass loss from similar coatings. The considerable scatter, in turn, tends to negate the advantages of measuring the powdering so accurately.
The visual rating technique therefore lacks accuracy, and the mass loss technique lacks precision. In addition, correlations between visual rating schemes and mass loss measurements tend to be poor, suggesting that the different coating variables affect these results somewhat differently. Because of these difficulties in analyzing powdering test data, Applicants' prior attempts to develop methods to accurately predict powdering rating and mass loss have not been successful.
Since the existing techniques of basic trial-and-error or hunch-based parameter determination are less than satisfactory, there is a continuing need in the art of manufacturing galvanneal-coated steel, for a method of determining product parameters based on analysis and prediction of galvanneal powdering in different powdering tests.
A need also exists for a method of analyzing data on galvanneal powdering, and for predicting, based on such data, the quality of galvanneal powdering resistance which can be expected in response to different powdering tests, as a function of certain parameters. The need for such prediction capabilities extends not only into the determination of product parameters at the product development stage, prior to actual production, but also into process control during production.
Since some galvanneal-coated steel products will react differently depending on which powdering test is being performed, there also is a need in the art for a method of classifying such products based on a prediction of their responses to the different powdering tests.